Title :
An algorithm for scalable clustering: Ensemble Rapid Centroid Estimation
Author :
Yuwono, Mitchell ; Sir, Steven W. ; Moulton, Brace D. ; Ying Guo ; Nguyen, Hung T.
Author_Institution :
Fac. of Eng. & Inf. Technol., Univ. of Technol., Sydney, NSW, Australia
Abstract :
This paper describes a new algorithm, called Ensemble Rapid Centroid Estimation (ERCE), designed to handle large-scale non-convex cluster optimization tasks, and estimate the number of clusters with quasi-linear complexity. ERCE stems from a recently developed Rapid Centroid Estimation (RCE) algorithm. RCE was originally developed as a lightweight simplification of the Particle Swarm Clustering (PSC) algorithm. RCE retained the quality of PSC, greatly reduced the computational complexity, and increased the stability. However, RCE has certain limitations with respect to complexity, and is unsuitable for non-convex clusters. The new ERCE algorithm presented here addresses these limitations.
Keywords :
computational complexity; concave programming; particle swarm optimisation; pattern clustering; ERCE algorithm; PSC algorithm; cluster number estimation; computational complexity reduction; ensemble rapid centroid estimation algorithm; large-scale nonconvex cluster optimization tasks; particle swarm clustering algorithm; quasilinear complexity; scalable clustering algorithm; Algorithm design and analysis; Clustering algorithms; Computational complexity; Estimation; Indexes; Particle swarm optimization; Vectors;
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
DOI :
10.1109/CEC.2014.6900295